Observational studies and their utility for practice.
Bibliographic record
Abstract
Randomised controlled clinical trials are the best source of evidence for assessing the efficacy of drugs.Observational studies provide critical descriptive data and information on long-term efficacy and safety that clinical trials cannot provide, at generally much less expense.Observational studies include case reports and case series, ecological studies, cross-sectional studies, case-control studies and cohort studies.New and ongoing developments in data and analytical technology, such as data linkage and propensity score matching, offer a promising future for observational studies.However, no study design or statistical method can account for confounders and bias in the way that randomised controlled trials can.Clinical registries are gaining importance as a method to monitor and improve the quality of care in Australia.Although registries are a form of cohort study, clinical trials can be incorporated into them to exploit the routine follow-up of patients to capture relevant outcomes. Ecological studiesEcological studies are based on analysis of aggregated data at group levels (for example populations), and do not involve data on individuals.These data can be analysed descriptively, but not definitively for causation.Typical examples include studies that examine patterns of drug use over time.One example is the comparison of the use of non-steroidal anti-inflammatory drugs and COX-2 inhibitors in Australia and Canada. 11Sometimes ecological studies describe associations between drugs and outcomes, such as changes in the rates of upper gastrointestinal haemorrhage after the introduction of COX-2 inhibitors. 12However, because individual-level data are not presented, causality is at best only implied in ecological studies.The 'ecological fallacy' refers to the error of assuming that associations observed in ecological studies are causal when they are not. Cross-sectional studiesCross-sectional studies collect data at a single point in time for each single individual, but the actual data collection may take place over a period of time or on more than one occasion.There is no longitudinal follow-up of individuals.Cross-sectional studies represent the archetypal descriptive study. 1 Typically, they provide a profile of a population of interest, which may be broad, like the Australian Health Survey undertaken intermittently by the Australian Bureau of Statistics, 13 or focused on specific populations, such as older Australians. 14
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.356 | 0.628 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".